{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 Physical GPUs, 2 Logical GPUs\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import graphgallery \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# Set if memory growth should be enabled for ALL `PhysicalDevice`.\n",
    "graphgallery.set_memory_growth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.1.2'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.4.0'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graphgallery.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the Datasets\n",
    "+ cora\n",
    "+ citeseer\n",
    "+ pubmed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from graphgallery.data import Planetoid\n",
    "\n",
    "# set `verbose=False` to avoid these printed tables\n",
    "data = Planetoid('cora', root=\"~/GraphData/datasets/\", verbose=False)\n",
    "graph = data.graph\n",
    "idx_train, idx_val, idx_test = data.split()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'citeseer', 'cora', 'pubmed'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.supported_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training...\n",
      "100/100 [==============================] - 2s 17ms/step - loss: 1.0161 - acc: 0.9500 - val_loss: 1.4101 - val_acc: 0.7740 - time: 1.7035\n",
      "Testing...\n",
      "1/1 [==============================] - 0s 76ms/step - test_loss: 1.4123 - test_acc: 0.8120 - time: 0.0763\n",
      "Test loss 1.4123, Test accuracy 81.20%\n"
     ]
    }
   ],
   "source": [
    "from graphgallery.nn.models import GCN\n",
    "model = GCN(graph, device='GPU', attr_transform=\"normalize_attr\", seed=123)\n",
    "model.build()\n",
    "# train with validation\n",
    "his = model.train(idx_train, idx_val, verbose=1, epochs=100)\n",
    "# train without validation\n",
    "# his = model.train(idx_train, verbose=1, epochs=100)\n",
    "loss, accuracy = model.test(idx_test)\n",
    "print(f'Test loss {loss:.5}, Test accuracy {accuracy:.2%}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show model summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"gcn\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "attr_matrix (InputLayer)        [(None, 1433)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "adj_matrix (InputLayer)         [(None, None)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "graph_convolution (GraphConvolu (None, 16)           22928       attr_matrix[0][0]                \n",
      "                                                                 adj_matrix[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 16)           0           graph_convolution[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "graph_convolution_1 (GraphConvo (None, 7)            112         dropout[0][0]                    \n",
      "                                                                 adj_matrix[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "node_index (InputLayer)         [(None,)]            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "gather (Gather)                 (None, 7)            0           graph_convolution_1[0][0]        \n",
      "                                                                 node_index[0][0]                 \n",
      "==================================================================================================\n",
      "Total params: 23,040\n",
      "Trainable params: 23,040\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "with plt.style.context(['science', 'no-latex']):\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
    "    axes[0].plot(his.history['acc'], label='Train accuracy')\n",
    "    axes[0].plot(his.history['val_acc'], label='Val accuracy')\n",
    "    axes[0].legend()\n",
    "    axes[0].set_title('Accuracy')\n",
    "    axes[0].set_xlabel('Epochs')\n",
    "    axes[0].set_ylabel('Accuracy')\n",
    "\n",
    "\n",
    "    axes[1].plot(his.history['loss'], label='Training loss')\n",
    "    axes[1].plot(his.history['val_loss'], label='Validation loss')\n",
    "    axes[1].legend()\n",
    "    axes[1].set_title('Loss')\n",
    "    axes[1].set_xlabel('Epochs')\n",
    "    axes[1].set_ylabel('Loss')\n",
    "    \n",
    "    plt.autoscale(tight=True)\n",
    "    plt.show()    "
   ]
  }
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